- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
- 🍖 原作者：K同学啊 | 接辅导、项目定制
- 🚀 文章来源：K同学的学习圈子
文章目录
- 前言
- 1 我的环境
- 2 pytorch实现DenseNet算法
- 2.1 前期准备
- 2.1.1 引入库
- 2.1.2 设置GPU（如果设备上支持GPU就使用GPU,否则使用CPU）
- 2.1.3 导入数据
- 2.1.4 可视化数据
- 2.1.4 图像数据变换
- 2.1.4 划分数据集
- 2.1.4 加载数据
- 2.1.4 查看数据
- 2.2 搭建densenet121模型
- 2.3 训练模型
- 2.3.1 设置超参数
- 2.3.2 编写训练函数
- 2.3.3 编写测试函数
- 2.3.4 正式训练
- 2.4 结果可视化
- 2.4 指定图片进行预测
- 2.6 模型评估
- 3 tensorflow实现DenseNet算法
- 3.1.引入库
- 3.2.设置GPU（如果使用的是CPU可以忽略这步）
- 3.3.导入数据
- 3.4.查看数据
- 3.5.加载数据
- 3.6.再次检查数据
- 3.7.配置数据集
- 3.8.可视化数据
- 3.9.构建DenseNet网络
- 3.10.编译模型
- 3.11.训练模型
- 3.12.模型评估
- 3.13.图像预测
- 4 知识点详解
- 4.1 DenseNet算法详解
- 4.1.1 前言
- 4.1.2 设计理念
- 4.1.2.1 标准神经网络
- 4.1.2.2 ResNet
- 4.1.2.3 DenseNet
- 4.1.3 网络结构
- 4.1.4 效果对比
- 4.1.5 使用Pytroch实现DenseNet121
- 总结
前言
关键字： pytorch实现DenseNet算法，tensorflow实现DenseNet算法，DenseNet算法详解
1 我的环境
- 电脑系统：Windows 11
- 语言环境：python 3.8.6
- 编译器：pycharm2020.2.3
- 深度学习环境：
torch == 1.9.1+cu111
torchvision == 0.10.1+cu111
TensorFlow 2.10.1 - 显卡：NVIDIA GeForce RTX 4070
2 pytorch实现DenseNet算法
2.1 前期准备
2.1.1 引入库
import torch
import torch.nn as nn
import time
import copy
from torchvision import transforms, datasets
from pathlib import Path
from PIL import Image
import torchsummary as summary
import torch.nn.functional as F
from collections import OrderedDict
import re
import torch.utils.model_zoo as model_zoo
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 # 分辨率
import warnings
warnings.filterwarnings('ignore') # 忽略一些warning内容，无需打印
2.1.2 设置GPU（如果设备上支持GPU就使用GPU,否则使用CPU）
"""前期准备-设置GPU"""
# 如果设备上支持GPU就使用GPU,否则使用CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using {} device".format(device))
输出
Using cuda device
2.1.3 导入数据
'''前期工作-导入数据'''
data_dir = r"D:\DeepLearning\data\bird\bird_photos"
data_dir = Path(data_dir)
data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[-1] for path in data_paths]
print(classeNames)
输出
['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']
2.1.4 可视化数据
'''前期工作-可视化数据'''
subfolder = Path(data_dir) / "Cockatoo"
image_files = list(p.resolve() for p in subfolder.glob('*') if p.suffix in [".jpg", ".png", ".jpeg"])
plt.figure(figsize=(10, 6))
for i in range(len(image_files[:12])):
image_file = image_files[i]
ax = plt.subplot(3, 4, i + 1)
img = Image.open(str(image_file))
plt.imshow(img)
plt.axis("off")
# 显示图片
plt.tight_layout()
plt.show()
2.1.4 图像数据变换
'''前期工作-图像数据变换'''
total_datadir = data_dir
# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([
transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]) # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])
total_data = datasets.ImageFolder(total_datadir, transform=train_transforms)
print(total_data)
print(total_data.class_to_idx)
输出
Dataset ImageFolder
Number of datapoints: 565
Root location: D:\DeepLearning\data\bird\bird_photos
StandardTransform
Transform: Compose(
Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)
{'Bananaquit': 0, 'Black Skimmer': 1, 'Black Throated Bushtiti': 2, 'Cockatoo': 3}
2.1.4 划分数据集
'''前期工作-划分数据集'''
train_size = int(0.8 * len(total_data)) # train_size表示训练集大小，通过将总体数据长度的80%转换为整数得到；
test_size = len(total_data) - train_size # test_size表示测试集大小，是总体数据长度减去训练集大小。
# 使用torch.utils.data.random_split()方法进行数据集划分。该方法将总体数据total_data按照指定的大小比例（[train_size, test_size]）随机划分为训练集和测试集，
# 并将划分结果分别赋值给train_dataset和test_dataset两个变量。
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print("train_dataset={}\ntest_dataset={}".format(train_dataset, test_dataset))
print("train_size={}\ntest_size={}".format(train_size, test_size))
输出
train_dataset=<torch.utils.data.dataset.Subset object at 0x000001309DFA26D0>
test_dataset=<torch.utils.data.dataset.Subset object at 0x000001309DFA2760>
train_size=452
test_size=113
2.1.4 加载数据
'''前期工作-加载数据'''
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
2.1.4 查看数据
'''前期工作-查看数据'''
for X, y in test_dl:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
输出
Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int64
2.2 搭建densenet121模型
"""构建DenseNet网络"""
# 这里我们采用了Pytorch的框架来实现DenseNet，
# 首先实现DenseBlock中的内部结构，这里是BN+ReLU+1×1Conv+BN+ReLU+3×3Conv结构，最后也加入dropout层用于训练过程。
class _DenseLayer(nn.Sequential):
"""Basic unit of DenseBlock (using bottleneck layer) """
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate,
kernel_size=1, stride=1, bias=False)),
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
kernel_size=3, stride=1, padding=1, bias=False)),
self.drop_rate = drop_rate
def forward(self, x):
new_features = super(_DenseLayer, self).forward(x)
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return torch.cat([x, new_features], 1)
# 实现DenseBlock模块，内部是密集连接方式（输入特征数线性增长）：
class _DenseBlock(nn.Sequential):
"""DenseBlock """
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(
num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
self.add_module('denselayer%d' % (i + 1), layer)
# 实现Transition层，它主要是一个卷积层和一个池化层：
class _Transition(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm2d(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
kernel_size=1, stride=1, bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
# 最后我们实现DenseNet网络：
class DenseNet(nn.Module):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 3 or 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
"""
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=24, bn_size=4, compression=0.5, drop_rate=0,
num_classes=1000):
super(DenseNet, self).__init__()
# First Conv2d
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
]))
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)
self.features.add_module('denseblock%d' % (i + 1), block)
num_features += num_layers * growth_rate
if i != len(block_config) - 1:
transition = _Transition(num_input_features=num_features,
num_output_features=int(num_features * compression))
self.features.add_module('transition%d' % (i + 1), transition)
num_features = int(num_features * compression)
# Final bn+relu
self.features.add_module('norm5', nn.BatchNorm2d(num_features))
self.features.add_module('relu5', nn.ReLU(inplace=True))
# classification layer
self.classifier = nn.Linear(num_features, num_classes)
# params initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
def forward(self, x):
features = self.features(x)
out = F.avg_pool2d(features, 7, stride=1).view(features.size(0), -1)
out = self.classifier(out)
return out
model_urls = {
'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth'}
def densenet121(pretrained=False, **kwargs):
"""DenseNet121"""
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
if pretrained:
# '.'s are no longer allowed in module names, but pervious _DenseLayer
# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
# They are also in the checkpoints in model_urls. This pattern is used
# to find such keys.
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
state_dict = model_zoo.load_url(model_urls['densenet121'])
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
return model
"""搭建densenet121模型"""
# model = densenet121().to(device)
model = densenet121(True).to(device) # 使用预训练模型
print(model)
print(summary.summary(model, (3, 224, 224))) # 查看模型的参数量以及相关指标
输出
DenseNet(
(features): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu0): ReLU(inplace=True)
(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(denseblock1): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition1): _Transition(
(norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock2): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition2): _Transition(
(norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock3): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer17): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer18): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer19): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer20): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer21): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer22): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer23): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer24): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(transition3): _Transition(
(norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock4): _DenseBlock(
(denselayer1): _DenseLayer(
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer2): _DenseLayer(
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer3): _DenseLayer(
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer4): _DenseLayer(
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer5): _DenseLayer(
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer6): _DenseLayer(
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer7): _DenseLayer(
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer8): _DenseLayer(
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer9): _DenseLayer(
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer10): _DenseLayer(
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer11): _DenseLayer(
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer12): _DenseLayer(
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer13): _DenseLayer(
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer14): _DenseLayer(
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer15): _DenseLayer(
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(denselayer16): _DenseLayer(
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu1): ReLU(inplace=True)
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu5): ReLU(inplace=True)
)
(classifier): Linear(in_features=1024, out_features=1000, bias=True)
)
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
BatchNorm2d-5 [-1, 64, 56, 56] 128
ReLU-6 [-1, 64, 56, 56] 0
Conv2d-7 [-1, 128, 56, 56] 8,192
BatchNorm2d-8 [-1, 128, 56, 56] 256
ReLU-9 [-1, 128, 56, 56] 0
Conv2d-10 [-1, 32, 56, 56] 36,864
BatchNorm2d-11 [-1, 96, 56, 56] 192
ReLU-12 [-1, 96, 56, 56] 0
Conv2d-13 [-1, 128, 56, 56] 12,288
BatchNorm2d-14 [-1, 128, 56, 56] 256
ReLU-15 [-1, 128, 56, 56] 0
Conv2d-16 [-1, 32, 56, 56] 36,864
BatchNorm2d-17 [-1, 128, 56, 56] 256
ReLU-18 [-1, 128, 56, 56] 0
Conv2d-19 [-1, 128, 56, 56] 16,384
BatchNorm2d-20 [-1, 128, 56, 56] 256
ReLU-21 [-1, 128, 56, 56] 0
Conv2d-22 [-1, 32, 56, 56] 36,864
BatchNorm2d-23 [-1, 160, 56, 56] 320
ReLU-24 [-1, 160, 56, 56] 0
Conv2d-25 [-1, 128, 56, 56] 20,480
BatchNorm2d-26 [-1, 128, 56, 56] 256
ReLU-27 [-1, 128, 56, 56] 0
Conv2d-28 [-1, 32, 56, 56] 36,864
BatchNorm2d-29 [-1, 192, 56, 56] 384
ReLU-30 [-1, 192, 56, 56] 0
Conv2d-31 [-1, 128, 56, 56] 24,576
BatchNorm2d-32 [-1, 128, 56, 56] 256
ReLU-33 [-1, 128, 56, 56] 0
Conv2d-34 [-1, 32, 56, 56] 36,864
BatchNorm2d-35 [-1, 224, 56, 56] 448
ReLU-36 [-1, 224, 56, 56] 0
Conv2d-37 [-1, 128, 56, 56] 28,672
BatchNorm2d-38 [-1, 128, 56, 56] 256
ReLU-39 [-1, 128, 56, 56] 0
Conv2d-40 [-1, 32, 56, 56] 36,864
BatchNorm2d-41 [-1, 256, 56, 56] 512
ReLU-42 [-1, 256, 56, 56] 0
Conv2d-43 [-1, 128, 56, 56] 32,768
AvgPool2d-44 [-1, 128, 28, 28] 0
BatchNorm2d-45 [-1, 128, 28, 28] 256
ReLU-46 [-1, 128, 28, 28] 0
Conv2d-47 [-1, 128, 28, 28] 16,384
BatchNorm2d-48 [-1, 128, 28, 28] 256
ReLU-49 [-1, 128, 28, 28] 0
Conv2d-50 [-1, 32, 28, 28] 36,864
BatchNorm2d-51 [-1, 160, 28, 28] 320
ReLU-52 [-1, 160, 28, 28] 0
Conv2d-53 [-1, 128, 28, 28] 20,480
BatchNorm2d-54 [-1, 128, 28, 28] 256
ReLU-55 [-1, 128, 28, 28] 0
Conv2d-56 [-1, 32, 28, 28] 36,864
BatchNorm2d-57 [-1, 192, 28, 28] 384
ReLU-58 [-1, 192, 28, 28] 0
Conv2d-59 [-1, 128, 28, 28] 24,576
BatchNorm2d-60 [-1, 128, 28, 28] 256
ReLU-61 [-1, 128, 28, 28] 0
Conv2d-62 [-1, 32, 28, 28] 36,864
BatchNorm2d-63 [-1, 224, 28, 28] 448
ReLU-64 [-1, 224, 28, 28] 0
Conv2d-65 [-1, 128, 28, 28] 28,672
BatchNorm2d-66 [-1, 128, 28, 28] 256
ReLU-67 [-1, 128, 28, 28] 0
Conv2d-68 [-1, 32, 28, 28] 36,864
BatchNorm2d-69 [-1, 256, 28, 28] 512
ReLU-70 [-1, 256, 28, 28] 0
Conv2d-71 [-1, 128, 28, 28] 32,768
BatchNorm2d-72 [-1, 128, 28, 28] 256
ReLU-73 [-1, 128, 28, 28] 0
Conv2d-74 [-1, 32, 28, 28] 36,864
BatchNorm2d-75 [-1, 288, 28, 28] 576
ReLU-76 [-1, 288, 28, 28] 0
Conv2d-77 [-1, 128, 28, 28] 36,864
BatchNorm2d-78 [-1, 128, 28, 28] 256
ReLU-79 [-1, 128, 28, 28] 0
Conv2d-80 [-1, 32, 28, 28] 36,864
BatchNorm2d-81 [-1, 320, 28, 28] 640
ReLU-82 [-1, 320, 28, 28] 0
Conv2d-83 [-1, 128, 28, 28] 40,960
BatchNorm2d-84 [-1, 128, 28, 28] 256
ReLU-85 [-1, 128, 28, 28] 0
Conv2d-86 [-1, 32, 28, 28] 36,864
BatchNorm2d-87 [-1, 352, 28, 28] 704
ReLU-88 [-1, 352, 28, 28] 0
Conv2d-89 [-1, 128, 28, 28] 45,056
BatchNorm2d-90 [-1, 128, 28, 28] 256
ReLU-91 [-1, 128, 28, 28] 0
Conv2d-92 [-1, 32, 28, 28] 36,864
BatchNorm2d-93 [-1, 384, 28, 28] 768
ReLU-94 [-1, 384, 28, 28] 0
Conv2d-95 [-1, 128, 28, 28] 49,152
BatchNorm2d-96 [-1, 128, 28, 28] 256
ReLU-97 [-1, 128, 28, 28] 0
Conv2d-98 [-1, 32, 28, 28] 36,864
BatchNorm2d-99 [-1, 416, 28, 28] 832
ReLU-100 [-1, 416, 28, 28] 0
Conv2d-101 [-1, 128, 28, 28] 53,248
BatchNorm2d-102 [-1, 128, 28, 28] 256
ReLU-103 [-1, 128, 28, 28] 0
Conv2d-104 [-1, 32, 28, 28] 36,864
BatchNorm2d-105 [-1, 448, 28, 28] 896
ReLU-106 [-1, 448, 28, 28] 0
Conv2d-107 [-1, 128, 28, 28] 57,344
BatchNorm2d-108 [-1, 128, 28, 28] 256
ReLU-109 [-1, 128, 28, 28] 0
Conv2d-110 [-1, 32, 28, 28] 36,864
BatchNorm2d-111 [-1, 480, 28, 28] 960
ReLU-112 [-1, 480, 28, 28] 0
Conv2d-113 [-1, 128, 28, 28] 61,440
BatchNorm2d-114 [-1, 128, 28, 28] 256
ReLU-115 [-1, 128, 28, 28] 0
Conv2d-116 [-1, 32, 28, 28] 36,864
BatchNorm2d-117 [-1, 512, 28, 28] 1,024
ReLU-118 [-1, 512, 28, 28] 0
Conv2d-119 [-1, 256, 28, 28] 131,072
AvgPool2d-120 [-1, 256, 14, 14] 0
BatchNorm2d-121 [-1, 256, 14, 14] 512
ReLU-122 [-1, 256, 14, 14] 0
Conv2d-123 [-1, 128, 14, 14] 32,768
BatchNorm2d-124 [-1, 128, 14, 14] 256
ReLU-125 [-1, 128, 14, 14] 0
Conv2d-126 [-1, 32, 14, 14] 36,864
BatchNorm2d-127 [-1, 288, 14, 14] 576
ReLU-128 [-1, 288, 14, 14] 0
Conv2d-129 [-1, 128, 14, 14] 36,864
BatchNorm2d-130 [-1, 128, 14, 14] 256
ReLU-131 [-1, 128, 14, 14] 0
Conv2d-132 [-1, 32, 14, 14] 36,864
BatchNorm2d-133 [-1, 320, 14, 14] 640
ReLU-134 [-1, 320, 14, 14] 0
Conv2d-135 [-1, 128, 14, 14] 40,960
BatchNorm2d-136 [-1, 128, 14, 14] 256
ReLU-137 [-1, 128, 14, 14] 0
Conv2d-138 [-1, 32, 14, 14] 36,864
BatchNorm2d-139 [-1, 352, 14, 14] 704
ReLU-140 [-1, 352, 14, 14] 0
Conv2d-141 [-1, 128, 14, 14] 45,056
BatchNorm2d-142 [-1, 128, 14, 14] 256
ReLU-143 [-1, 128, 14, 14] 0
Conv2d-144 [-1, 32, 14, 14] 36,864
BatchNorm2d-145 [-1, 384, 14, 14] 768
ReLU-146 [-1, 384, 14, 14] 0
Conv2d-147 [-1, 128, 14, 14] 49,152
BatchNorm2d-148 [-1, 128, 14, 14] 256
ReLU-149 [-1, 128, 14, 14] 0
Conv2d-150 [-1, 32, 14, 14] 36,864
BatchNorm2d-151 [-1, 416, 14, 14] 832
ReLU-152 [-1, 416, 14, 14] 0
Conv2d-153 [-1, 128, 14, 14] 53,248
BatchNorm2d-154 [-1, 128, 14, 14] 256
ReLU-155 [-1, 128, 14, 14] 0
Conv2d-156 [-1, 32, 14, 14] 36,864
BatchNorm2d-157 [-1, 448, 14, 14] 896
ReLU-158 [-1, 448, 14, 14] 0
Conv2d-159 [-1, 128, 14, 14] 57,344
BatchNorm2d-160 [-1, 128, 14, 14] 256
ReLU-161 [-1, 128, 14, 14] 0
Conv2d-162 [-1, 32, 14, 14] 36,864
BatchNorm2d-163 [-1, 480, 14, 14] 960
ReLU-164 [-1, 480, 14, 14] 0
Conv2d-165 [-1, 128, 14, 14] 61,440
BatchNorm2d-166 [-1, 128, 14, 14] 256
ReLU-167 [-1, 128, 14, 14] 0
Conv2d-168 [-1, 32, 14, 14] 36,864
BatchNorm2d-169 [-1, 512, 14, 14] 1,024
ReLU-170 [-1, 512, 14, 14] 0
Conv2d-171 [-1, 128, 14, 14] 65,536
BatchNorm2d-172 [-1, 128, 14, 14] 256
ReLU-173 [-1, 128, 14, 14] 0
Conv2d-174 [-1, 32, 14, 14] 36,864
BatchNorm2d-175 [-1, 544, 14, 14] 1,088
ReLU-176 [-1, 544, 14, 14] 0
Conv2d-177 [-1, 128, 14, 14] 69,632
BatchNorm2d-178 [-1, 128, 14, 14] 256
ReLU-179 [-1, 128, 14, 14] 0
Conv2d-180 [-1, 32, 14, 14] 36,864
BatchNorm2d-181 [-1, 576, 14, 14] 1,152
ReLU-182 [-1, 576, 14, 14] 0
Conv2d-183 [-1, 128, 14, 14] 73,728
BatchNorm2d-184 [-1, 128, 14, 14] 256
ReLU-185 [-1, 128, 14, 14] 0
Conv2d-186 [-1, 32, 14, 14] 36,864
BatchNorm2d-187 [-1, 608, 14, 14] 1,216
ReLU-188 [-1, 608, 14, 14] 0
Conv2d-189 [-1, 128, 14, 14] 77,824
BatchNorm2d-190 [-1, 128, 14, 14] 256
ReLU-191 [-1, 128, 14, 14] 0
Conv2d-192 [-1, 32, 14, 14] 36,864
BatchNorm2d-193 [-1, 640, 14, 14] 1,280
ReLU-194 [-1, 640, 14, 14] 0
Conv2d-195 [-1, 128, 14, 14] 81,920
BatchNorm2d-196 [-1, 128, 14, 14] 256
ReLU-197 [-1, 128, 14, 14] 0
Conv2d-198 [-1, 32, 14, 14] 36,864
BatchNorm2d-199 [-1, 672, 14, 14] 1,344
ReLU-200 [-1, 672, 14, 14] 0
Conv2d-201 [-1, 128, 14, 14] 86,016
BatchNorm2d-202 [-1, 128, 14, 14] 256
ReLU-203 [-1, 128, 14, 14] 0
Conv2d-204 [-1, 32, 14, 14] 36,864
BatchNorm2d-205 [-1, 704, 14, 14] 1,408
ReLU-206 [-1, 704, 14, 14] 0
Conv2d-207 [-1, 128, 14, 14] 90,112
BatchNorm2d-208 [-1, 128, 14, 14] 256
ReLU-209 [-1, 128, 14, 14] 0
Conv2d-210 [-1, 32, 14, 14] 36,864
BatchNorm2d-211 [-1, 736, 14, 14] 1,472
ReLU-212 [-1, 736, 14, 14] 0
Conv2d-213 [-1, 128, 14, 14] 94,208
BatchNorm2d-214 [-1, 128, 14, 14] 256
ReLU-215 [-1, 128, 14, 14] 0
Conv2d-216 [-1, 32, 14, 14] 36,864
BatchNorm2d-217 [-1, 768, 14, 14] 1,536
ReLU-218 [-1, 768, 14, 14] 0
Conv2d-219 [-1, 128, 14, 14] 98,304
BatchNorm2d-220 [-1, 128, 14, 14] 256
ReLU-221 [-1, 128, 14, 14] 0
Conv2d-222 [-1, 32, 14, 14] 36,864
BatchNorm2d-223 [-1, 800, 14, 14] 1,600
ReLU-224 [-1, 800, 14, 14] 0
Conv2d-225 [-1, 128, 14, 14] 102,400
BatchNorm2d-226 [-1, 128, 14, 14] 256
ReLU-227 [-1, 128, 14, 14] 0
Conv2d-228 [-1, 32, 14, 14] 36,864
BatchNorm2d-229 [-1, 832, 14, 14] 1,664
ReLU-230 [-1, 832, 14, 14] 0
Conv2d-231 [-1, 128, 14, 14] 106,496
BatchNorm2d-232 [-1, 128, 14, 14] 256
ReLU-233 [-1, 128, 14, 14] 0
Conv2d-234 [-1, 32, 14, 14] 36,864
BatchNorm2d-235 [-1, 864, 14, 14] 1,728
ReLU-236 [-1, 864, 14, 14] 0
Conv2d-237 [-1, 128, 14, 14] 110,592
BatchNorm2d-238 [-1, 128, 14, 14] 256
ReLU-239 [-1, 128, 14, 14] 0
Conv2d-240 [-1, 32, 14, 14] 36,864
BatchNorm2d-241 [-1, 896, 14, 14] 1,792
ReLU-242 [-1, 896, 14, 14] 0
Conv2d-243 [-1, 128, 14, 14] 114,688
BatchNorm2d-244 [-1, 128, 14, 14] 256
ReLU-245 [-1, 128, 14, 14] 0
Conv2d-246 [-1, 32, 14, 14] 36,864
BatchNorm2d-247 [-1, 928, 14, 14] 1,856
ReLU-248 [-1, 928, 14, 14] 0
Conv2d-249 [-1, 128, 14, 14] 118,784
BatchNorm2d-250 [-1, 128, 14, 14] 256
ReLU-251 [-1, 128, 14, 14] 0
Conv2d-252 [-1, 32, 14, 14] 36,864
BatchNorm2d-253 [-1, 960, 14, 14] 1,920
ReLU-254 [-1, 960, 14, 14] 0
Conv2d-255 [-1, 128, 14, 14] 122,880
BatchNorm2d-256 [-1, 128, 14, 14] 256
ReLU-257 [-1, 128, 14, 14] 0
Conv2d-258 [-1, 32, 14, 14] 36,864
BatchNorm2d-259 [-1, 992, 14, 14] 1,984
ReLU-260 [-1, 992, 14, 14] 0
Conv2d-261 [-1, 128, 14, 14] 126,976
BatchNorm2d-262 [-1, 128, 14, 14] 256
ReLU-263 [-1, 128, 14, 14] 0
Conv2d-264 [-1, 32, 14, 14] 36,864
BatchNorm2d-265 [-1, 1024, 14, 14] 2,048
ReLU-266 [-1, 1024, 14, 14] 0
Conv2d-267 [-1, 512, 14, 14] 524,288
AvgPool2d-268 [-1, 512, 7, 7] 0
BatchNorm2d-269 [-1, 512, 7, 7] 1,024
ReLU-270 [-1, 512, 7, 7] 0
Conv2d-271 [-1, 128, 7, 7] 65,536
BatchNorm2d-272 [-1, 128, 7, 7] 256
ReLU-273 [-1, 128, 7, 7] 0
Conv2d-274 [-1, 32, 7, 7] 36,864
BatchNorm2d-275 [-1, 544, 7, 7] 1,088
ReLU-276 [-1, 544, 7, 7] 0
Conv2d-277 [-1, 128, 7, 7] 69,632
BatchNorm2d-278 [-1, 128, 7, 7] 256
ReLU-279 [-1, 128, 7, 7] 0
Conv2d-280 [-1, 32, 7, 7] 36,864
BatchNorm2d-281 [-1, 576, 7, 7] 1,152
ReLU-282 [-1, 576, 7, 7] 0
Conv2d-283 [-1, 128, 7, 7] 73,728
BatchNorm2d-284 [-1, 128, 7, 7] 256
ReLU-285 [-1, 128, 7, 7] 0
Conv2d-286 [-1, 32, 7, 7] 36,864
BatchNorm2d-287 [-1, 608, 7, 7] 1,216
ReLU-288 [-1, 608, 7, 7] 0
Conv2d-289 [-1, 128, 7, 7] 77,824
BatchNorm2d-290 [-1, 128, 7, 7] 256
ReLU-291 [-1, 128, 7, 7] 0
Conv2d-292 [-1, 32, 7, 7] 36,864
BatchNorm2d-293 [-1, 640, 7, 7] 1,280
ReLU-294 [-1, 640, 7, 7] 0
Conv2d-295 [-1, 128, 7, 7] 81,920
BatchNorm2d-296 [-1, 128, 7, 7] 256
ReLU-297 [-1, 128, 7, 7] 0
Conv2d-298 [-1, 32, 7, 7] 36,864
BatchNorm2d-299 [-1, 672, 7, 7] 1,344
ReLU-300 [-1, 672, 7, 7] 0
Conv2d-301 [-1, 128, 7, 7] 86,016
BatchNorm2d-302 [-1, 128, 7, 7] 256
ReLU-303 [-1, 128, 7, 7] 0
Conv2d-304 [-1, 32, 7, 7] 36,864
BatchNorm2d-305 [-1, 704, 7, 7] 1,408
ReLU-306 [-1, 704, 7, 7] 0
Conv2d-307 [-1, 128, 7, 7] 90,112
BatchNorm2d-308 [-1, 128, 7, 7] 256
ReLU-309 [-1, 128, 7, 7] 0
Conv2d-310 [-1, 32, 7, 7] 36,864
BatchNorm2d-311 [-1, 736, 7, 7] 1,472
ReLU-312 [-1, 736, 7, 7] 0
Conv2d-313 [-1, 128, 7, 7] 94,208
BatchNorm2d-314 [-1, 128, 7, 7] 256
ReLU-315 [-1, 128, 7, 7] 0
Conv2d-316 [-1, 32, 7, 7] 36,864
BatchNorm2d-317 [-1, 768, 7, 7] 1,536
ReLU-318 [-1, 768, 7, 7] 0
Conv2d-319 [-1, 128, 7, 7] 98,304
BatchNorm2d-320 [-1, 128, 7, 7] 256
ReLU-321 [-1, 128, 7, 7] 0
Conv2d-322 [-1, 32, 7, 7] 36,864
BatchNorm2d-323 [-1, 800, 7, 7] 1,600
ReLU-324 [-1, 800, 7, 7] 0
Conv2d-325 [-1, 128, 7, 7] 102,400
BatchNorm2d-326 [-1, 128, 7, 7] 256
ReLU-327 [-1, 128, 7, 7] 0
Conv2d-328 [-1, 32, 7, 7] 36,864
BatchNorm2d-329 [-1, 832, 7, 7] 1,664
ReLU-330 [-1, 832, 7, 7] 0
Conv2d-331 [-1, 128, 7, 7] 106,496
BatchNorm2d-332 [-1, 128, 7, 7] 256
ReLU-333 [-1, 128, 7, 7] 0
Conv2d-334 [-1, 32, 7, 7] 36,864
BatchNorm2d-335 [-1, 864, 7, 7] 1,728
ReLU-336 [-1, 864, 7, 7] 0
Conv2d-337 [-1, 128, 7, 7] 110,592
BatchNorm2d-338 [-1, 128, 7, 7] 256
ReLU-339 [-1, 128, 7, 7] 0
Conv2d-340 [-1, 32, 7, 7] 36,864
BatchNorm2d-341 [-1, 896, 7, 7] 1,792
ReLU-342 [-1, 896, 7, 7] 0
Conv2d-343 [-1, 128, 7, 7] 114,688
BatchNorm2d-344 [-1, 128, 7, 7] 256
ReLU-345 [-1, 128, 7, 7] 0
Conv2d-346 [-1, 32, 7, 7] 36,864
BatchNorm2d-347 [-1, 928, 7, 7] 1,856
ReLU-348 [-1, 928, 7, 7] 0
Conv2d-349 [-1, 128, 7, 7] 118,784
BatchNorm2d-350 [-1, 128, 7, 7] 256
ReLU-351 [-1, 128, 7, 7] 0
Conv2d-352 [-1, 32, 7, 7] 36,864
BatchNorm2d-353 [-1, 960, 7, 7] 1,920
ReLU-354 [-1, 960, 7, 7] 0
Conv2d-355 [-1, 128, 7, 7] 122,880
BatchNorm2d-356 [-1, 128, 7, 7] 256
ReLU-357 [-1, 128, 7, 7] 0
Conv2d-358 [-1, 32, 7, 7] 36,864
BatchNorm2d-359 [-1, 992, 7, 7] 1,984
ReLU-360 [-1, 992, 7, 7] 0
Conv2d-361 [-1, 128, 7, 7] 126,976
BatchNorm2d-362 [-1, 128, 7, 7] 256
ReLU-363 [-1, 128, 7, 7] 0
Conv2d-364 [-1, 32, 7, 7] 36,864
BatchNorm2d-365 [-1, 1024, 7, 7] 2,048
ReLU-366 [-1, 1024, 7, 7] 0
Linear-367 [-1, 1000] 1,025,000
================================================================
Total params: 7,978,856
Trainable params: 7,978,856
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.58
Params size (MB): 30.44
Estimated Total Size (MB): 325.59
----------------------------------------------------------------
2.3 训练模型
2.3.1 设置超参数
"""训练模型--设置超参数"""
loss_fn = nn.CrossEntropyLoss() # 创建损失函数，计算实际输出和真实相差多少，交叉熵损失函数，事实上，它就是做图片分类任务时常用的损失函数
learn_rate = 1e-4 # 学习率
optimizer1 = torch.optim.SGD(model.parameters(), lr=learn_rate)# 作用是定义优化器，用来训练时候优化模型参数；其中，SGD表示随机梯度下降，用于控制实际输出y与真实y之间的相差有多大
optimizer2 = torch.optim.Adam(model.parameters(), lr=learn_rate)
lr_opt = optimizer2
model_opt = optimizer2
# 调用官方动态学习率接口时使用2
lambda1 = lambda epoch : 0.92 ** (epoch // 4)
# optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(lr_opt, lr_lambda=lambda1) #选定调整方法
2.3.2 编写训练函数
"""训练模型--编写训练函数"""
# 训练循环
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) # 训练集的大小，一共60000张图片
num_batches = len(dataloader) # 批次数目，1875（60000/32）
train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
for X, y in dataloader: # 加载数据加载器，得到里面的 X（图片数据）和 y（真实标签）
X, y = X.to(device), y.to(device) # 用于将数据存到显卡
# 计算预测误差
pred = model(X) # 网络输出
loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
# 反向传播
optimizer.zero_grad() # 清空过往梯度
loss.backward() # 反向传播，计算当前梯度
optimizer.step() # 根据梯度更新网络参数
# 记录acc与loss
train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc, train_loss
2.3.3 编写测试函数
"""训练模型--编写测试函数"""
# 测试函数和训练函数大致相同，但是由于不进行梯度下降对网络权重进行更新，所以不需要传入优化器
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset) # 测试集的大小，一共10000张图片
num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整）
test_loss, test_acc = 0, 0
# 当不进行训练时，停止梯度更新，节省计算内存消耗
with torch.no_grad(): # 测试时模型参数不用更新，所以 no_grad，整个模型参数正向推就ok，不反向更新参数
for imgs, target in dataloader:
imgs, target = imgs.to(device), target.to(device)
# 计算loss
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss += loss.item()
test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()#统计预测正确的个数
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
2.3.4 正式训练
"""训练模型--正式训练"""
epochs = 10
train_loss = []
train_acc = []
test_loss = []
test_acc = []
best_test_acc=0
for epoch in range(epochs):
milliseconds_t1 = int(time.time() * 1000)
# 更新学习率（使用自定义学习率时使用）
# adjust_learning_rate(lr_opt, epoch, learn_rate)
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, model_opt)
scheduler.step() # 更新学习率（调用官方动态学习率接口时使用）
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# 获取当前的学习率
lr = lr_opt.state_dict()['param_groups'][0]['lr']
milliseconds_t2 = int(time.time() * 1000)
template = ('Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}, Lr:{:.2E}')
if best_test_acc < epoch_test_acc:
best_test_acc = epoch_test_acc
#备份最好的模型
best_model = copy.deepcopy(model)
template = (
'Epoch:{:2d}, duration:{}ms, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}, Lr:{:.2E},Update the best model')
print(
template.format(epoch + 1, milliseconds_t2-milliseconds_t1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss, lr))
# 保存最佳模型到文件中
PATH = './best_model.pth' # 保存的参数文件名
torch.save(model.state_dict(), PATH)
print('Done')
输出最高精度为Test_acc:100%
Epoch: 1, duration:5339ms, Train_acc:32.5%, Train_loss:4.717, Test_acc:94.7%，Test_loss:0.666, Lr:1.00E-04,Update the best model
Epoch: 2, duration:4585ms, Train_acc:98.5%, Train_loss:0.255, Test_acc:98.2%，Test_loss:0.120, Lr:1.00E-04,Update the best model
Epoch: 3, duration:4651ms, Train_acc:100.0%, Train_loss:0.037, Test_acc:99.1%，Test_loss:0.057, Lr:1.00E-04,Update the best model
Epoch: 4, duration:4610ms, Train_acc:99.8%, Train_loss:0.039, Test_acc:100.0%，Test_loss:0.040, Lr:1.00E-04,Update the best model
Epoch: 5, duration:4520ms, Train_acc:99.8%, Train_loss:0.032, Test_acc:100.0%，Test_loss:0.047, Lr:1.00E-04
Epoch: 6, duration:4528ms, Train_acc:100.0%, Train_loss:0.055, Test_acc:100.0%，Test_loss:0.038, Lr:1.00E-04
Epoch: 7, duration:4541ms, Train_acc:100.0%, Train_loss:0.021, Test_acc:100.0%，Test_loss:0.022, Lr:1.00E-04
Epoch: 8, duration:4568ms, Train_acc:100.0%, Train_loss:0.066, Test_acc:100.0%，Test_loss:0.018, Lr:1.00E-04
Epoch: 9, duration:4515ms, Train_acc:99.8%, Train_loss:0.084, Test_acc:100.0%，Test_loss:0.022, Lr:1.00E-04
Epoch:10, duration:4602ms, Train_acc:99.6%, Train_loss:0.136, Test_acc:100.0%，Test_loss:0.028, Lr:1.00E-04
这里使用了预训练模型，效果特别好
2.4 结果可视化
"""训练模型--结果可视化"""
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
2.4 指定图片进行预测
def predict_one_image(image_path, model, transform, classes):
test_img = Image.open(image_path).convert('RGB')
plt.imshow(test_img) # 展示预测的图片
plt.show()
test_img = transform(test_img)
img = test_img.to(device).unsqueeze(0)
model.eval()
output = model(img)
_, pred = torch.max(output, 1)
pred_class = classes[pred]
print(f'预测结果是：{pred_class}')
# 将参数加载到model当中
model.load_state_dict(torch.load(PATH, map_location=device))
"""指定图片进行预测"""
classes = list(total_data.class_to_idx)
# 预测训练集中的某张照片
predict_one_image(image_path=str(Path(data_dir) / "Cockatoo/001.jpg"),
model=model,
transform=train_transforms,
classes=classes)
输出
预测结果是：Cockatoo
2.6 模型评估
"""模型评估"""
best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
# 查看是否与我们记录的最高准确率一致
print(epoch_test_acc, epoch_test_loss)
输出
1.0 0.05105462612118572
3 tensorflow实现DenseNet算法
3.1.引入库
from PIL import Image
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
import tensorflow as tf
from keras import layers, models, Input
from keras.layers import Input, Activation, BatchNormalization, Flatten
from keras.layers import Dense, Conv2D, MaxPooling2D, ZeroPadding2D, GlobalMaxPooling2D, AveragePooling2D, Flatten, \
Dropout, BatchNormalization, GlobalAveragePooling2D
from keras.models import Model
from keras import regularizers
from tensorflow import keras
from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') # 忽略一些warning内容，无需打印
3.2.设置GPU（如果使用的是CPU可以忽略这步）
'''前期工作-设置GPU（如果使用的是CPU可以忽略这步）'''
# 检查GPU是否可用
print(tf.test.is_built_with_cuda())
gpus = tf.config.list_physical_devices("GPU")
print(gpus)
if gpus:
gpu0 = gpus[0] # 如果有多个GPU，仅使用第0个GPU
tf.config.experimental.set_memory_growth(gpu0, True) # 设置GPU显存用量按需使用
tf.config.set_visible_devices([gpu0], "GPU")
执行结果
True
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
3.3.导入数据
'''前期工作-导入数据'''
data_dir = r"D:\DeepLearning\data\bird\bird_photos"
data_dir = Path(data_dir)
3.4.查看数据
'''前期工作-查看数据'''
image_count = len(list(data_dir.glob('*/*.jpg')))
print("图片总数为：", image_count)
image_list = list(data_dir.glob('Bananaquit/*.jpg'))
image = Image.open(str(image_list[1]))
# 查看图像实例的属性
print(image.format, image.size, image.mode)
plt.imshow(image)
plt.axis("off")
plt.show()
执行结果：
图片总数为： 565
JPEG (224, 224) RGB
3.5.加载数据
'''数据预处理-加载数据'''
batch_size = 32
img_height = 224
img_width = 224
"""
关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789
"""
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)
运行结果：
Found 565 files belonging to 4 classes.
Using 452 files for training.
Found 565 files belonging to 4 classes.
Using 113 files for validation.
['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']
3.6.再次检查数据
'''数据预处理-再次检查数据'''
# Image_batch是形状的张量(16, 336, 336, 3)。这是一批形状336x336x3的16张图片（最后一维指的是彩色通道RGB）。
# Label_batch是形状（16，）的张量，这些标签对应16张图片
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
运行结果
(32, 224, 224, 3)
(32,)
3.7.配置数据集
'''数据预处理-配置数据集'''
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
3.8.可视化数据
'''数据预处理-可视化数据'''
plt.figure(figsize=(10, 5))
for images, labels in train_ds.take(1):
for i in range(8):
ax = plt.subplot(2, 4, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]], fontsize=10)
plt.axis("off")
# 显示图片
plt.show()
3.9.构建DenseNet网络
"""构建DenseNet网络"""
def conv_fn(x, growth_rate):
x1 = keras.layers.BatchNormalization()(x)
x1 = keras.layers.Activation('relu')(x)
x1 = keras.layers.Activation('relu')(x1)
x1 = keras.layers.Conv2D(4 * growth_rate, 1, 1, padding="same", use_bias=False)(x1)
x1 = keras.layers.BatchNormalization()(x1)
x1 = keras.layers.Activation("relu")(x1)
x1 = keras.layers.Conv2D(growth_rate, 3, 1, padding="same", use_bias=False)(x1)
return keras.layers.Concatenate(axis=3)([x, x1])
def dense_block(x, block, growth_rate=32):
for i in range(block):
x = conv_fn(x, growth_rate)
return x
k = keras.backend
def trans_block(x, theta):
x1 = keras.layers.BatchNormalization()(x)
x1 = keras.layers.Activation("relu")(x1)
x1 = keras.layers.Conv2D(int(k.int_shape(x)[3] * theta), 1, 1, use_bias=False)(x1)
x1 = keras.layers.AveragePooling2D(pool_size=(2, 2), strides=2, padding="valid")(x1)
return x1
def densenet(input_shape, block, n_classes=1000):
# 56*56*64
x_input = keras.layers.Input(shape=input_shape)
x = keras.layers.Conv2D(64, kernel_size=(7, 7), strides=2, padding="same", use_bias=False)(x_input)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)
x = dense_block(x, block[0])
x = trans_block(x, 0.5) # 28*28
x = dense_block(x, block[1])
x = trans_block(x, 0.5) # 14*14
x = dense_block(x, block[2])
x = trans_block(x, 0.5) # 7*7
x = dense_block(x, block[3])
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Activation("relu")(x)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(n_classes, activation="softmax")(x)
model = keras.models.Model(inputs=[x_input], outputs=[outputs])
return model
model_121 = densenet([224, 224, 3], [6, 12, 24, 16]) # DenseNet-121
model_169 = densenet([224, 224, 3], [6, 12, 32, 32]) # DenseNet-169
model_201 = densenet([224, 224, 3], [6, 12, 48, 32]) # DenseNet-201
model_269 = densenet([224, 224, 3], [6, 12, 64, 48]) # DenseNet-269
model = model_121
model.summary()
网络结构结果如下：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 224, 224, 3 0 []
)]
conv2d (Conv2D) (None, 112, 112, 64 9408 ['input_1[0][0]']
)
batch_normalization (BatchNorm (None, 112, 112, 64 256 ['conv2d[0][0]']
alization) )
max_pooling2d (MaxPooling2D) (None, 56, 56, 64) 0 ['batch_normalization[0][0]']
activation (Activation) (None, 56, 56, 64) 0 ['max_pooling2d[0][0]']
activation_1 (Activation) (None, 56, 56, 64) 0 ['activation[0][0]']
conv2d_1 (Conv2D) (None, 56, 56, 128) 8192 ['activation_1[0][0]']
batch_normalization_2 (BatchNo (None, 56, 56, 128) 512 ['conv2d_1[0][0]']
rmalization)
activation_2 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_2[0][0]']
conv2d_2 (Conv2D) (None, 56, 56, 32) 36864 ['activation_2[0][0]']
concatenate (Concatenate) (None, 56, 56, 96) 0 ['max_pooling2d[0][0]',
'conv2d_2[0][0]']
activation_3 (Activation) (None, 56, 56, 96) 0 ['concatenate[0][0]']
activation_4 (Activation) (None, 56, 56, 96) 0 ['activation_3[0][0]']
conv2d_3 (Conv2D) (None, 56, 56, 128) 12288 ['activation_4[0][0]']
batch_normalization_4 (BatchNo (None, 56, 56, 128) 512 ['conv2d_3[0][0]']
rmalization)
activation_5 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_4[0][0]']
conv2d_4 (Conv2D) (None, 56, 56, 32) 36864 ['activation_5[0][0]']
concatenate_1 (Concatenate) (None, 56, 56, 128) 0 ['concatenate[0][0]',
'conv2d_4[0][0]']
activation_6 (Activation) (None, 56, 56, 128) 0 ['concatenate_1[0][0]']
activation_7 (Activation) (None, 56, 56, 128) 0 ['activation_6[0][0]']
conv2d_5 (Conv2D) (None, 56, 56, 128) 16384 ['activation_7[0][0]']
batch_normalization_6 (BatchNo (None, 56, 56, 128) 512 ['conv2d_5[0][0]']
rmalization)
activation_8 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_6[0][0]']
conv2d_6 (Conv2D) (None, 56, 56, 32) 36864 ['activation_8[0][0]']
concatenate_2 (Concatenate) (None, 56, 56, 160) 0 ['concatenate_1[0][0]',
'conv2d_6[0][0]']
activation_9 (Activation) (None, 56, 56, 160) 0 ['concatenate_2[0][0]']
activation_10 (Activation) (None, 56, 56, 160) 0 ['activation_9[0][0]']
conv2d_7 (Conv2D) (None, 56, 56, 128) 20480 ['activation_10[0][0]']
batch_normalization_8 (BatchNo (None, 56, 56, 128) 512 ['conv2d_7[0][0]']
rmalization)
activation_11 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_8[0][0]']
conv2d_8 (Conv2D) (None, 56, 56, 32) 36864 ['activation_11[0][0]']
concatenate_3 (Concatenate) (None, 56, 56, 192) 0 ['concatenate_2[0][0]',
'conv2d_8[0][0]']
activation_12 (Activation) (None, 56, 56, 192) 0 ['concatenate_3[0][0]']
activation_13 (Activation) (None, 56, 56, 192) 0 ['activation_12[0][0]']
conv2d_9 (Conv2D) (None, 56, 56, 128) 24576 ['activation_13[0][0]']
batch_normalization_10 (BatchN (None, 56, 56, 128) 512 ['conv2d_9[0][0]']
ormalization)
activation_14 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_10[0][0]']
conv2d_10 (Conv2D) (None, 56, 56, 32) 36864 ['activation_14[0][0]']
concatenate_4 (Concatenate) (None, 56, 56, 224) 0 ['concatenate_3[0][0]',
'conv2d_10[0][0]']
activation_15 (Activation) (None, 56, 56, 224) 0 ['concatenate_4[0][0]']
activation_16 (Activation) (None, 56, 56, 224) 0 ['activation_15[0][0]']
conv2d_11 (Conv2D) (None, 56, 56, 128) 28672 ['activation_16[0][0]']
batch_normalization_12 (BatchN (None, 56, 56, 128) 512 ['conv2d_11[0][0]']
ormalization)
activation_17 (Activation) (None, 56, 56, 128) 0 ['batch_normalization_12[0][0]']
conv2d_12 (Conv2D) (None, 56, 56, 32) 36864 ['activation_17[0][0]']
concatenate_5 (Concatenate) (None, 56, 56, 256) 0 ['concatenate_4[0][0]',
'conv2d_12[0][0]']
batch_normalization_13 (BatchN (None, 56, 56, 256) 1024 ['concatenate_5[0][0]']
ormalization)
activation_18 (Activation) (None, 56, 56, 256) 0 ['batch_normalization_13[0][0]']
conv2d_13 (Conv2D) (None, 56, 56, 128) 32768 ['activation_18[0][0]']
average_pooling2d (AveragePool (None, 28, 28, 128) 0 ['conv2d_13[0][0]']
ing2D)
activation_19 (Activation) (None, 28, 28, 128) 0 ['average_pooling2d[0][0]']
activation_20 (Activation) (None, 28, 28, 128) 0 ['activation_19[0][0]']
conv2d_14 (Conv2D) (None, 28, 28, 128) 16384 ['activation_20[0][0]']
batch_normalization_15 (BatchN (None, 28, 28, 128) 512 ['conv2d_14[0][0]']
ormalization)
activation_21 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_15[0][0]']
conv2d_15 (Conv2D) (None, 28, 28, 32) 36864 ['activation_21[0][0]']
concatenate_6 (Concatenate) (None, 28, 28, 160) 0 ['average_pooling2d[0][0]',
'conv2d_15[0][0]']
activation_22 (Activation) (None, 28, 28, 160) 0 ['concatenate_6[0][0]']
activation_23 (Activation) (None, 28, 28, 160) 0 ['activation_22[0][0]']
conv2d_16 (Conv2D) (None, 28, 28, 128) 20480 ['activation_23[0][0]']
batch_normalization_17 (BatchN (None, 28, 28, 128) 512 ['conv2d_16[0][0]']
ormalization)
activation_24 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_17[0][0]']
conv2d_17 (Conv2D) (None, 28, 28, 32) 36864 ['activation_24[0][0]']
concatenate_7 (Concatenate) (None, 28, 28, 192) 0 ['concatenate_6[0][0]',
'conv2d_17[0][0]']
activation_25 (Activation) (None, 28, 28, 192) 0 ['concatenate_7[0][0]']
activation_26 (Activation) (None, 28, 28, 192) 0 ['activation_25[0][0]']
conv2d_18 (Conv2D) (None, 28, 28, 128) 24576 ['activation_26[0][0]']
batch_normalization_19 (BatchN (None, 28, 28, 128) 512 ['conv2d_18[0][0]']
ormalization)
activation_27 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_19[0][0]']
conv2d_19 (Conv2D) (None, 28, 28, 32) 36864 ['activation_27[0][0]']
concatenate_8 (Concatenate) (None, 28, 28, 224) 0 ['concatenate_7[0][0]',
'conv2d_19[0][0]']
activation_28 (Activation) (None, 28, 28, 224) 0 ['concatenate_8[0][0]']
activation_29 (Activation) (None, 28, 28, 224) 0 ['activation_28[0][0]']
conv2d_20 (Conv2D) (None, 28, 28, 128) 28672 ['activation_29[0][0]']
batch_normalization_21 (BatchN (None, 28, 28, 128) 512 ['conv2d_20[0][0]']
ormalization)
activation_30 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_21[0][0]']
conv2d_21 (Conv2D) (None, 28, 28, 32) 36864 ['activation_30[0][0]']
concatenate_9 (Concatenate) (None, 28, 28, 256) 0 ['concatenate_8[0][0]',
'conv2d_21[0][0]']
activation_31 (Activation) (None, 28, 28, 256) 0 ['concatenate_9[0][0]']
activation_32 (Activation) (None, 28, 28, 256) 0 ['activation_31[0][0]']
conv2d_22 (Conv2D) (None, 28, 28, 128) 32768 ['activation_32[0][0]']
batch_normalization_23 (BatchN (None, 28, 28, 128) 512 ['conv2d_22[0][0]']
ormalization)
activation_33 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_23[0][0]']
conv2d_23 (Conv2D) (None, 28, 28, 32) 36864 ['activation_33[0][0]']
concatenate_10 (Concatenate) (None, 28, 28, 288) 0 ['concatenate_9[0][0]',
'conv2d_23[0][0]']
activation_34 (Activation) (None, 28, 28, 288) 0 ['concatenate_10[0][0]']
activation_35 (Activation) (None, 28, 28, 288) 0 ['activation_34[0][0]']
conv2d_24 (Conv2D) (None, 28, 28, 128) 36864 ['activation_35[0][0]']
batch_normalization_25 (BatchN (None, 28, 28, 128) 512 ['conv2d_24[0][0]']
ormalization)
activation_36 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_25[0][0]']
conv2d_25 (Conv2D) (None, 28, 28, 32) 36864 ['activation_36[0][0]']
concatenate_11 (Concatenate) (None, 28, 28, 320) 0 ['concatenate_10[0][0]',
'conv2d_25[0][0]']
activation_37 (Activation) (None, 28, 28, 320) 0 ['concatenate_11[0][0]']
activation_38 (Activation) (None, 28, 28, 320) 0 ['activation_37[0][0]']
conv2d_26 (Conv2D) (None, 28, 28, 128) 40960 ['activation_38[0][0]']
batch_normalization_27 (BatchN (None, 28, 28, 128) 512 ['conv2d_26[0][0]']
ormalization)
activation_39 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_27[0][0]']
conv2d_27 (Conv2D) (None, 28, 28, 32) 36864 ['activation_39[0][0]']
concatenate_12 (Concatenate) (None, 28, 28, 352) 0 ['concatenate_11[0][0]',
'conv2d_27[0][0]']
activation_40 (Activation) (None, 28, 28, 352) 0 ['concatenate_12[0][0]']
activation_41 (Activation) (None, 28, 28, 352) 0 ['activation_40[0][0]']
conv2d_28 (Conv2D) (None, 28, 28, 128) 45056 ['activation_41[0][0]']
batch_normalization_29 (BatchN (None, 28, 28, 128) 512 ['conv2d_28[0][0]']
ormalization)
activation_42 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_29[0][0]']
conv2d_29 (Conv2D) (None, 28, 28, 32) 36864 ['activation_42[0][0]']
concatenate_13 (Concatenate) (None, 28, 28, 384) 0 ['concatenate_12[0][0]',
'conv2d_29[0][0]']
activation_43 (Activation) (None, 28, 28, 384) 0 ['concatenate_13[0][0]']
activation_44 (Activation) (None, 28, 28, 384) 0 ['activation_43[0][0]']
conv2d_30 (Conv2D) (None, 28, 28, 128) 49152 ['activation_44[0][0]']
batch_normalization_31 (BatchN (None, 28, 28, 128) 512 ['conv2d_30[0][0]']
ormalization)
activation_45 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_31[0][0]']
conv2d_31 (Conv2D) (None, 28, 28, 32) 36864 ['activation_45[0][0]']
concatenate_14 (Concatenate) (None, 28, 28, 416) 0 ['concatenate_13[0][0]',
'conv2d_31[0][0]']
activation_46 (Activation) (None, 28, 28, 416) 0 ['concatenate_14[0][0]']
activation_47 (Activation) (None, 28, 28, 416) 0 ['activation_46[0][0]']
conv2d_32 (Conv2D) (None, 28, 28, 128) 53248 ['activation_47[0][0]']
batch_normalization_33 (BatchN (None, 28, 28, 128) 512 ['conv2d_32[0][0]']
ormalization)
activation_48 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_33[0][0]']
conv2d_33 (Conv2D) (None, 28, 28, 32) 36864 ['activation_48[0][0]']
concatenate_15 (Concatenate) (None, 28, 28, 448) 0 ['concatenate_14[0][0]',
'conv2d_33[0][0]']
activation_49 (Activation) (None, 28, 28, 448) 0 ['concatenate_15[0][0]']
activation_50 (Activation) (None, 28, 28, 448) 0 ['activation_49[0][0]']
conv2d_34 (Conv2D) (None, 28, 28, 128) 57344 ['activation_50[0][0]']
batch_normalization_35 (BatchN (None, 28, 28, 128) 512 ['conv2d_34[0][0]']
ormalization)
activation_51 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_35[0][0]']
conv2d_35 (Conv2D) (None, 28, 28, 32) 36864 ['activation_51[0][0]']
concatenate_16 (Concatenate) (None, 28, 28, 480) 0 ['concatenate_15[0][0]',
'conv2d_35[0][0]']
activation_52 (Activation) (None, 28, 28, 480) 0 ['concatenate_16[0][0]']
activation_53 (Activation) (None, 28, 28, 480) 0 ['activation_52[0][0]']
conv2d_36 (Conv2D) (None, 28, 28, 128) 61440 ['activation_53[0][0]']
batch_normalization_37 (BatchN (None, 28, 28, 128) 512 ['conv2d_36[0][0]']
ormalization)
activation_54 (Activation) (None, 28, 28, 128) 0 ['batch_normalization_37[0][0]']
conv2d_37 (Conv2D) (None, 28, 28, 32) 36864 ['activation_54[0][0]']
concatenate_17 (Concatenate) (None, 28, 28, 512) 0 ['concatenate_16[0][0]',
'conv2d_37[0][0]']
batch_normalization_38 (BatchN (None, 28, 28, 512) 2048 ['concatenate_17[0][0]']
ormalization)
activation_55 (Activation) (None, 28, 28, 512) 0 ['batch_normalization_38[0][0]']
conv2d_38 (Conv2D) (None, 28, 28, 256) 131072 ['activation_55[0][0]']
average_pooling2d_1 (AveragePo (None, 14, 14, 256) 0 ['conv2d_38[0][0]']
oling2D)
activation_56 (Activation) (None, 14, 14, 256) 0 ['average_pooling2d_1[0][0]']
activation_57 (Activation) (None, 14, 14, 256) 0 ['activation_56[0][0]']
conv2d_39 (Conv2D) (None, 14, 14, 128) 32768 ['activation_57[0][0]']
batch_normalization_40 (BatchN (None, 14, 14, 128) 512 ['conv2d_39[0][0]']
ormalization)
activation_58 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_40[0][0]']
conv2d_40 (Conv2D) (None, 14, 14, 32) 36864 ['activation_58[0][0]']
concatenate_18 (Concatenate) (None, 14, 14, 288) 0 ['average_pooling2d_1[0][0]',
'conv2d_40[0][0]']
activation_59 (Activation) (None, 14, 14, 288) 0 ['concatenate_18[0][0]']
activation_60 (Activation) (None, 14, 14, 288) 0 ['activation_59[0][0]']
conv2d_41 (Conv2D) (None, 14, 14, 128) 36864 ['activation_60[0][0]']
batch_normalization_42 (BatchN (None, 14, 14, 128) 512 ['conv2d_41[0][0]']
ormalization)
activation_61 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_42[0][0]']
conv2d_42 (Conv2D) (None, 14, 14, 32) 36864 ['activation_61[0][0]']
concatenate_19 (Concatenate) (None, 14, 14, 320) 0 ['concatenate_18[0][0]',
'conv2d_42[0][0]']
activation_62 (Activation) (None, 14, 14, 320) 0 ['concatenate_19[0][0]']
activation_63 (Activation) (None, 14, 14, 320) 0 ['activation_62[0][0]']
conv2d_43 (Conv2D) (None, 14, 14, 128) 40960 ['activation_63[0][0]']
batch_normalization_44 (BatchN (None, 14, 14, 128) 512 ['conv2d_43[0][0]']
ormalization)
activation_64 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_44[0][0]']
conv2d_44 (Conv2D) (None, 14, 14, 32) 36864 ['activation_64[0][0]']
concatenate_20 (Concatenate) (None, 14, 14, 352) 0 ['concatenate_19[0][0]',
'conv2d_44[0][0]']
activation_65 (Activation) (None, 14, 14, 352) 0 ['concatenate_20[0][0]']
activation_66 (Activation) (None, 14, 14, 352) 0 ['activation_65[0][0]']
conv2d_45 (Conv2D) (None, 14, 14, 128) 45056 ['activation_66[0][0]']
batch_normalization_46 (BatchN (None, 14, 14, 128) 512 ['conv2d_45[0][0]']
ormalization)
activation_67 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_46[0][0]']
conv2d_46 (Conv2D) (None, 14, 14, 32) 36864 ['activation_67[0][0]']
concatenate_21 (Concatenate) (None, 14, 14, 384) 0 ['concatenate_20[0][0]',
'conv2d_46[0][0]']
activation_68 (Activation) (None, 14, 14, 384) 0 ['concatenate_21[0][0]']
activation_69 (Activation) (None, 14, 14, 384) 0 ['activation_68[0][0]']
conv2d_47 (Conv2D) (None, 14, 14, 128) 49152 ['activation_69[0][0]']
batch_normalization_48 (BatchN (None, 14, 14, 128) 512 ['conv2d_47[0][0]']
ormalization)
activation_70 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_48[0][0]']
conv2d_48 (Conv2D) (None, 14, 14, 32) 36864 ['activation_70[0][0]']
concatenate_22 (Concatenate) (None, 14, 14, 416) 0 ['concatenate_21[0][0]',
'conv2d_48[0][0]']
activation_71 (Activation) (None, 14, 14, 416) 0 ['concatenate_22[0][0]']
activation_72 (Activation) (None, 14, 14, 416) 0 ['activation_71[0][0]']
conv2d_49 (Conv2D) (None, 14, 14, 128) 53248 ['activation_72[0][0]']
batch_normalization_50 (BatchN (None, 14, 14, 128) 512 ['conv2d_49[0][0]']
ormalization)
activation_73 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_50[0][0]']
conv2d_50 (Conv2D) (None, 14, 14, 32) 36864 ['activation_73[0][0]']
concatenate_23 (Concatenate) (None, 14, 14, 448) 0 ['concatenate_22[0][0]',
'conv2d_50[0][0]']
activation_74 (Activation) (None, 14, 14, 448) 0 ['concatenate_23[0][0]']
activation_75 (Activation) (None, 14, 14, 448) 0 ['activation_74[0][0]']
conv2d_51 (Conv2D) (None, 14, 14, 128) 57344 ['activation_75[0][0]']
batch_normalization_52 (BatchN (None, 14, 14, 128) 512 ['conv2d_51[0][0]']
ormalization)
activation_76 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_52[0][0]']
conv2d_52 (Conv2D) (None, 14, 14, 32) 36864 ['activation_76[0][0]']
concatenate_24 (Concatenate) (None, 14, 14, 480) 0 ['concatenate_23[0][0]',
'conv2d_52[0][0]']
activation_77 (Activation) (None, 14, 14, 480) 0 ['concatenate_24[0][0]']
activation_78 (Activation) (None, 14, 14, 480) 0 ['activation_77[0][0]']
conv2d_53 (Conv2D) (None, 14, 14, 128) 61440 ['activation_78[0][0]']
batch_normalization_54 (BatchN (None, 14, 14, 128) 512 ['conv2d_53[0][0]']
ormalization)
activation_79 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_54[0][0]']
conv2d_54 (Conv2D) (None, 14, 14, 32) 36864 ['activation_79[0][0]']
concatenate_25 (Concatenate) (None, 14, 14, 512) 0 ['concatenate_24[0][0]',
'conv2d_54[0][0]']
activation_80 (Activation) (None, 14, 14, 512) 0 ['concatenate_25[0][0]']
activation_81 (Activation) (None, 14, 14, 512) 0 ['activation_80[0][0]']
conv2d_55 (Conv2D) (None, 14, 14, 128) 65536 ['activation_81[0][0]']
batch_normalization_56 (BatchN (None, 14, 14, 128) 512 ['conv2d_55[0][0]']
ormalization)
activation_82 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_56[0][0]']
conv2d_56 (Conv2D) (None, 14, 14, 32) 36864 ['activation_82[0][0]']
concatenate_26 (Concatenate) (None, 14, 14, 544) 0 ['concatenate_25[0][0]',
'conv2d_56[0][0]']
activation_83 (Activation) (None, 14, 14, 544) 0 ['concatenate_26[0][0]']
activation_84 (Activation) (None, 14, 14, 544) 0 ['activation_83[0][0]']
conv2d_57 (Conv2D) (None, 14, 14, 128) 69632 ['activation_84[0][0]']
batch_normalization_58 (BatchN (None, 14, 14, 128) 512 ['conv2d_57[0][0]']
ormalization)
activation_85 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_58[0][0]']
conv2d_58 (Conv2D) (None, 14, 14, 32) 36864 ['activation_85[0][0]']
concatenate_27 (Concatenate) (None, 14, 14, 576) 0 ['concatenate_26[0][0]',
'conv2d_58[0][0]']
activation_86 (Activation) (None, 14, 14, 576) 0 ['concatenate_27[0][0]']
activation_87 (Activation) (None, 14, 14, 576) 0 ['activation_86[0][0]']
conv2d_59 (Conv2D) (None, 14, 14, 128) 73728 ['activation_87[0][0]']
batch_normalization_60 (BatchN (None, 14, 14, 128) 512 ['conv2d_59[0][0]']
ormalization)
activation_88 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_60[0][0]']
conv2d_60 (Conv2D) (None, 14, 14, 32) 36864 ['activation_88[0][0]']
concatenate_28 (Concatenate) (None, 14, 14, 608) 0 ['concatenate_27[0][0]',
'conv2d_60[0][0]']
activation_89 (Activation) (None, 14, 14, 608) 0 ['concatenate_28[0][0]']
activation_90 (Activation) (None, 14, 14, 608) 0 ['activation_89[0][0]']
conv2d_61 (Conv2D) (None, 14, 14, 128) 77824 ['activation_90[0][0]']
batch_normalization_62 (BatchN (None, 14, 14, 128) 512 ['conv2d_61[0][0]']
ormalization)
activation_91 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_62[0][0]']
conv2d_62 (Conv2D) (None, 14, 14, 32) 36864 ['activation_91[0][0]']
concatenate_29 (Concatenate) (None, 14, 14, 640) 0 ['concatenate_28[0][0]',
'conv2d_62[0][0]']
activation_92 (Activation) (None, 14, 14, 640) 0 ['concatenate_29[0][0]']
activation_93 (Activation) (None, 14, 14, 640) 0 ['activation_92[0][0]']
conv2d_63 (Conv2D) (None, 14, 14, 128) 81920 ['activation_93[0][0]']
batch_normalization_64 (BatchN (None, 14, 14, 128) 512 ['conv2d_63[0][0]']
ormalization)
activation_94 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_64[0][0]']
conv2d_64 (Conv2D) (None, 14, 14, 32) 36864 ['activation_94[0][0]']
concatenate_30 (Concatenate) (None, 14, 14, 672) 0 ['concatenate_29[0][0]',
'conv2d_64[0][0]']
activation_95 (Activation) (None, 14, 14, 672) 0 ['concatenate_30[0][0]']
activation_96 (Activation) (None, 14, 14, 672) 0 ['activation_95[0][0]']
conv2d_65 (Conv2D) (None, 14, 14, 128) 86016 ['activation_96[0][0]']
batch_normalization_66 (BatchN (None, 14, 14, 128) 512 ['conv2d_65[0][0]']
ormalization)
activation_97 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_66[0][0]']
conv2d_66 (Conv2D) (None, 14, 14, 32) 36864 ['activation_97[0][0]']
concatenate_31 (Concatenate) (None, 14, 14, 704) 0 ['concatenate_30[0][0]',
'conv2d_66[0][0]']
activation_98 (Activation) (None, 14, 14, 704) 0 ['concatenate_31[0][0]']
activation_99 (Activation) (None, 14, 14, 704) 0 ['activation_98[0][0]']
conv2d_67 (Conv2D) (None, 14, 14, 128) 90112 ['activation_99[0][0]']
batch_normalization_68 (BatchN (None, 14, 14, 128) 512 ['conv2d_67[0][0]']
ormalization)
activation_100 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_68[0][0]']
conv2d_68 (Conv2D) (None, 14, 14, 32) 36864 ['activation_100[0][0]']
concatenate_32 (Concatenate) (None, 14, 14, 736) 0 ['concatenate_31[0][0]',
'conv2d_68[0][0]']
activation_101 (Activation) (None, 14, 14, 736) 0 ['concatenate_32[0][0]']
activation_102 (Activation) (None, 14, 14, 736) 0 ['activation_101[0][0]']
conv2d_69 (Conv2D) (None, 14, 14, 128) 94208 ['activation_102[0][0]']
batch_normalization_70 (BatchN (None, 14, 14, 128) 512 ['conv2d_69[0][0]']
ormalization)
activation_103 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_70[0][0]']
conv2d_70 (Conv2D) (None, 14, 14, 32) 36864 ['activation_103[0][0]']
concatenate_33 (Concatenate) (None, 14, 14, 768) 0 ['concatenate_32[0][0]',
'conv2d_70[0][0]']
activation_104 (Activation) (None, 14, 14, 768) 0 ['concatenate_33[0][0]']
activation_105 (Activation) (None, 14, 14, 768) 0 ['activation_104[0][0]']
conv2d_71 (Conv2D) (None, 14, 14, 128) 98304 ['activation_105[0][0]']
batch_normalization_72 (BatchN (None, 14, 14, 128) 512 ['conv2d_71[0][0]']
ormalization)
activation_106 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_72[0][0]']
conv2d_72 (Conv2D) (None, 14, 14, 32) 36864 ['activation_106[0][0]']
concatenate_34 (Concatenate) (None, 14, 14, 800) 0 ['concatenate_33[0][0]',
'conv2d_72[0][0]']
activation_107 (Activation) (None, 14, 14, 800) 0 ['concatenate_34[0][0]']
activation_108 (Activation) (None, 14, 14, 800) 0 ['activation_107[0][0]']
conv2d_73 (Conv2D) (None, 14, 14, 128) 102400 ['activation_108[0][0]']
batch_normalization_74 (BatchN (None, 14, 14, 128) 512 ['conv2d_73[0][0]']
ormalization)
activation_109 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_74[0][0]']
conv2d_74 (Conv2D) (None, 14, 14, 32) 36864 ['activation_109[0][0]']
concatenate_35 (Concatenate) (None, 14, 14, 832) 0 ['concatenate_34[0][0]',
'conv2d_74[0][0]']
activation_110 (Activation) (None, 14, 14, 832) 0 ['concatenate_35[0][0]']
activation_111 (Activation) (None, 14, 14, 832) 0 ['activation_110[0][0]']
conv2d_75 (Conv2D) (None, 14, 14, 128) 106496 ['activation_111[0][0]']
batch_normalization_76 (BatchN (None, 14, 14, 128) 512 ['conv2d_75[0][0]']
ormalization)
activation_112 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_76[0][0]']
conv2d_76 (Conv2D) (None, 14, 14, 32) 36864 ['activation_112[0][0]']
concatenate_36 (Concatenate) (None, 14, 14, 864) 0 ['concatenate_35[0][0]',
'conv2d_76[0][0]']
activation_113 (Activation) (None, 14, 14, 864) 0 ['concatenate_36[0][0]']
activation_114 (Activation) (None, 14, 14, 864) 0 ['activation_113[0][0]']
conv2d_77 (Conv2D) (None, 14, 14, 128) 110592 ['activation_114[0][0]']
batch_normalization_78 (BatchN (None, 14, 14, 128) 512 ['conv2d_77[0][0]']
ormalization)
activation_115 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_78[0][0]']
conv2d_78 (Conv2D) (None, 14, 14, 32) 36864 ['activation_115[0][0]']
concatenate_37 (Concatenate) (None, 14, 14, 896) 0 ['concatenate_36[0][0]',
'conv2d_78[0][0]']
activation_116 (Activation) (None, 14, 14, 896) 0 ['concatenate_37[0][0]']
activation_117 (Activation) (None, 14, 14, 896) 0 ['activation_116[0][0]']
conv2d_79 (Conv2D) (None, 14, 14, 128) 114688 ['activation_117[0][0]']
batch_normalization_80 (BatchN (None, 14, 14, 128) 512 ['conv2d_79[0][0]']
ormalization)
activation_118 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_80[0][0]']
conv2d_80 (Conv2D) (None, 14, 14, 32) 36864 ['activation_118[0][0]']
concatenate_38 (Concatenate) (None, 14, 14, 928) 0 ['concatenate_37[0][0]',
'conv2d_80[0][0]']
activation_119 (Activation) (None, 14, 14, 928) 0 ['concatenate_38[0][0]']
activation_120 (Activation) (None, 14, 14, 928) 0 ['activation_119[0][0]']
conv2d_81 (Conv2D) (None, 14, 14, 128) 118784 ['activation_120[0][0]']
batch_normalization_82 (BatchN (None, 14, 14, 128) 512 ['conv2d_81[0][0]']
ormalization)
activation_121 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_82[0][0]']
conv2d_82 (Conv2D) (None, 14, 14, 32) 36864 ['activation_121[0][0]']
concatenate_39 (Concatenate) (None, 14, 14, 960) 0 ['concatenate_38[0][0]',
'conv2d_82[0][0]']
activation_122 (Activation) (None, 14, 14, 960) 0 ['concatenate_39[0][0]']
activation_123 (Activation) (None, 14, 14, 960) 0 ['activation_122[0][0]']
conv2d_83 (Conv2D) (None, 14, 14, 128) 122880 ['activation_123[0][0]']
batch_normalization_84 (BatchN (None, 14, 14, 128) 512 ['conv2d_83[0][0]']
ormalization)
activation_124 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_84[0][0]']
conv2d_84 (Conv2D) (None, 14, 14, 32) 36864 ['activation_124[0][0]']
concatenate_40 (Concatenate) (None, 14, 14, 992) 0 ['concatenate_39[0][0]',
'conv2d_84[0][0]']
activation_125 (Activation) (None, 14, 14, 992) 0 ['concatenate_40[0][0]']
activation_126 (Activation) (None, 14, 14, 992) 0 ['activation_125[0][0]']
conv2d_85 (Conv2D) (None, 14, 14, 128) 126976 ['activation_126[0][0]']
batch_normalization_86 (BatchN (None, 14, 14, 128) 512 ['conv2d_85[0][0]']
ormalization)
activation_127 (Activation) (None, 14, 14, 128) 0 ['batch_normalization_86[0][0]']
conv2d_86 (Conv2D) (None, 14, 14, 32) 36864 ['activation_127[0][0]']
concatenate_41 (Concatenate) (None, 14, 14, 1024 0 ['concatenate_40[0][0]',
) 'conv2d_86[0][0]']
batch_normalization_87 (BatchN (None, 14, 14, 1024 4096 ['concatenate_41[0][0]']
ormalization) )
activation_128 (Activation) (None, 14, 14, 1024 0 ['batch_normalization_87[0][0]']
)
conv2d_87 (Conv2D) (None, 14, 14, 512) 524288 ['activation_128[0][0]']
average_pooling2d_2 (AveragePo (None, 7, 7, 512) 0 ['conv2d_87[0][0]']
oling2D)
activation_129 (Activation) (None, 7, 7, 512) 0 ['average_pooling2d_2[0][0]']
activation_130 (Activation) (None, 7, 7, 512) 0 ['activation_129[0][0]']
conv2d_88 (Conv2D) (None, 7, 7, 128) 65536 ['activation_130[0][0]']
batch_normalization_89 (BatchN (None, 7, 7, 128) 512 ['conv2d_88[0][0]']
ormalization)
activation_131 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_89[0][0]']
conv2d_89 (Conv2D) (None, 7, 7, 32) 36864 ['activation_131[0][0]']
concatenate_42 (Concatenate) (None, 7, 7, 544) 0 ['average_pooling2d_2[0][0]',
'conv2d_89[0][0]']
activation_132 (Activation) (None, 7, 7, 544) 0 ['concatenate_42[0][0]']
activation_133 (Activation) (None, 7, 7, 544) 0 ['activation_132[0][0]']
conv2d_90 (Conv2D) (None, 7, 7, 128) 69632 ['activation_133[0][0]']
batch_normalization_91 (BatchN (None, 7, 7, 128) 512 ['conv2d_90[0][0]']
ormalization)
activation_134 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_91[0][0]']
conv2d_91 (Conv2D) (None, 7, 7, 32) 36864 ['activation_134[0][0]']
concatenate_43 (Concatenate) (None, 7, 7, 576) 0 ['concatenate_42[0][0]',
'conv2d_91[0][0]']
activation_135 (Activation) (None, 7, 7, 576) 0 ['concatenate_43[0][0]']
activation_136 (Activation) (None, 7, 7, 576) 0 ['activation_135[0][0]']
conv2d_92 (Conv2D) (None, 7, 7, 128) 73728 ['activation_136[0][0]']
batch_normalization_93 (BatchN (None, 7, 7, 128) 512 ['conv2d_92[0][0]']
ormalization)
activation_137 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_93[0][0]']
conv2d_93 (Conv2D) (None, 7, 7, 32) 36864 ['activation_137[0][0]']
concatenate_44 (Concatenate) (None, 7, 7, 608) 0 ['concatenate_43[0][0]',
'conv2d_93[0][0]']
activation_138 (Activation) (None, 7, 7, 608) 0 ['concatenate_44[0][0]']
activation_139 (Activation) (None, 7, 7, 608) 0 ['activation_138[0][0]']
conv2d_94 (Conv2D) (None, 7, 7, 128) 77824 ['activation_139[0][0]']
batch_normalization_95 (BatchN (None, 7, 7, 128) 512 ['conv2d_94[0][0]']
ormalization)
activation_140 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_95[0][0]']
conv2d_95 (Conv2D) (None, 7, 7, 32) 36864 ['activation_140[0][0]']
concatenate_45 (Concatenate) (None, 7, 7, 640) 0 ['concatenate_44[0][0]',
'conv2d_95[0][0]']
activation_141 (Activation) (None, 7, 7, 640) 0 ['concatenate_45[0][0]']
activation_142 (Activation) (None, 7, 7, 640) 0 ['activation_141[0][0]']
conv2d_96 (Conv2D) (None, 7, 7, 128) 81920 ['activation_142[0][0]']
batch_normalization_97 (BatchN (None, 7, 7, 128) 512 ['conv2d_96[0][0]']
ormalization)
activation_143 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_97[0][0]']
conv2d_97 (Conv2D) (None, 7, 7, 32) 36864 ['activation_143[0][0]']
concatenate_46 (Concatenate) (None, 7, 7, 672) 0 ['concatenate_45[0][0]',
'conv2d_97[0][0]']
activation_144 (Activation) (None, 7, 7, 672) 0 ['concatenate_46[0][0]']
activation_145 (Activation) (None, 7, 7, 672) 0 ['activation_144[0][0]']
conv2d_98 (Conv2D) (None, 7, 7, 128) 86016 ['activation_145[0][0]']
batch_normalization_99 (BatchN (None, 7, 7, 128) 512 ['conv2d_98[0][0]']
ormalization)
activation_146 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_99[0][0]']
conv2d_99 (Conv2D) (None, 7, 7, 32) 36864 ['activation_146[0][0]']
concatenate_47 (Concatenate) (None, 7, 7, 704) 0 ['concatenate_46[0][0]',
'conv2d_99[0][0]']
activation_147 (Activation) (None, 7, 7, 704) 0 ['concatenate_47[0][0]']
activation_148 (Activation) (None, 7, 7, 704) 0 ['activation_147[0][0]']
conv2d_100 (Conv2D) (None, 7, 7, 128) 90112 ['activation_148[0][0]']
batch_normalization_101 (Batch (None, 7, 7, 128) 512 ['conv2d_100[0][0]']
Normalization)
activation_149 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_101[0][0]']
conv2d_101 (Conv2D) (None, 7, 7, 32) 36864 ['activation_149[0][0]']
concatenate_48 (Concatenate) (None, 7, 7, 736) 0 ['concatenate_47[0][0]',
'conv2d_101[0][0]']
activation_150 (Activation) (None, 7, 7, 736) 0 ['concatenate_48[0][0]']
activation_151 (Activation) (None, 7, 7, 736) 0 ['activation_150[0][0]']
conv2d_102 (Conv2D) (None, 7, 7, 128) 94208 ['activation_151[0][0]']
batch_normalization_103 (Batch (None, 7, 7, 128) 512 ['conv2d_102[0][0]']
Normalization)
activation_152 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_103[0][0]']
conv2d_103 (Conv2D) (None, 7, 7, 32) 36864 ['activation_152[0][0]']
concatenate_49 (Concatenate) (None, 7, 7, 768) 0 ['concatenate_48[0][0]',
'conv2d_103[0][0]']
activation_153 (Activation) (None, 7, 7, 768) 0 ['concatenate_49[0][0]']
activation_154 (Activation) (None, 7, 7, 768) 0 ['activation_153[0][0]']
conv2d_104 (Conv2D) (None, 7, 7, 128) 98304 ['activation_154[0][0]']
batch_normalization_105 (Batch (None, 7, 7, 128) 512 ['conv2d_104[0][0]']
Normalization)
activation_155 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_105[0][0]']
conv2d_105 (Conv2D) (None, 7, 7, 32) 36864 ['activation_155[0][0]']
concatenate_50 (Concatenate) (None, 7, 7, 800) 0 ['concatenate_49[0][0]',
'conv2d_105[0][0]']
activation_156 (Activation) (None, 7, 7, 800) 0 ['concatenate_50[0][0]']
activation_157 (Activation) (None, 7, 7, 800) 0 ['activation_156[0][0]']
conv2d_106 (Conv2D) (None, 7, 7, 128) 102400 ['activation_157[0][0]']
batch_normalization_107 (Batch (None, 7, 7, 128) 512 ['conv2d_106[0][0]']
Normalization)
activation_158 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_107[0][0]']
conv2d_107 (Conv2D) (None, 7, 7, 32) 36864 ['activation_158[0][0]']
concatenate_51 (Concatenate) (None, 7, 7, 832) 0 ['concatenate_50[0][0]',
'conv2d_107[0][0]']
activation_159 (Activation) (None, 7, 7, 832) 0 ['concatenate_51[0][0]']
activation_160 (Activation) (None, 7, 7, 832) 0 ['activation_159[0][0]']
conv2d_108 (Conv2D) (None, 7, 7, 128) 106496 ['activation_160[0][0]']
batch_normalization_109 (Batch (None, 7, 7, 128) 512 ['conv2d_108[0][0]']
Normalization)
activation_161 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_109[0][0]']
conv2d_109 (Conv2D) (None, 7, 7, 32) 36864 ['activation_161[0][0]']
concatenate_52 (Concatenate) (None, 7, 7, 864) 0 ['concatenate_51[0][0]',
'conv2d_109[0][0]']
activation_162 (Activation) (None, 7, 7, 864) 0 ['concatenate_52[0][0]']
activation_163 (Activation) (None, 7, 7, 864) 0 ['activation_162[0][0]']
conv2d_110 (Conv2D) (None, 7, 7, 128) 110592 ['activation_163[0][0]']
batch_normalization_111 (Batch (None, 7, 7, 128) 512 ['conv2d_110[0][0]']
Normalization)
activation_164 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_111[0][0]']
conv2d_111 (Conv2D) (None, 7, 7, 32) 36864 ['activation_164[0][0]']
concatenate_53 (Concatenate) (None, 7, 7, 896) 0 ['concatenate_52[0][0]',
'conv2d_111[0][0]']
activation_165 (Activation) (None, 7, 7, 896) 0 ['concatenate_53[0][0]']
activation_166 (Activation) (None, 7, 7, 896) 0 ['activation_165[0][0]']
conv2d_112 (Conv2D) (None, 7, 7, 128) 114688 ['activation_166[0][0]']
batch_normalization_113 (Batch (None, 7, 7, 128) 512 ['conv2d_112[0][0]']
Normalization)
activation_167 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_113[0][0]']
conv2d_113 (Conv2D) (None, 7, 7, 32) 36864 ['activation_167[0][0]']
concatenate_54 (Concatenate) (None, 7, 7, 928) 0 ['concatenate_53[0][0]',
'conv2d_113[0][0]']
activation_168 (Activation) (None, 7, 7, 928) 0 ['concatenate_54[0][0]']
activation_169 (Activation) (None, 7, 7, 928) 0 ['activation_168[0][0]']
conv2d_114 (Conv2D) (None, 7, 7, 128) 118784 ['activation_169[0][0]']
batch_normalization_115 (Batch (None, 7, 7, 128) 512 ['conv2d_114[0][0]']
Normalization)
activation_170 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_115[0][0]']
conv2d_115 (Conv2D) (None, 7, 7, 32) 36864 ['activation_170[0][0]']
concatenate_55 (Concatenate) (None, 7, 7, 960) 0 ['concatenate_54[0][0]',
'conv2d_115[0][0]']
activation_171 (Activation) (None, 7, 7, 960) 0 ['concatenate_55[0][0]']
activation_172 (Activation) (None, 7, 7, 960) 0 ['activation_171[0][0]']
conv2d_116 (Conv2D) (None, 7, 7, 128) 122880 ['activation_172[0][0]']
batch_normalization_117 (Batch (None, 7, 7, 128) 512 ['conv2d_116[0][0]']
Normalization)
activation_173 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_117[0][0]']
conv2d_117 (Conv2D) (None, 7, 7, 32) 36864 ['activation_173[0][0]']
concatenate_56 (Concatenate) (None, 7, 7, 992) 0 ['concatenate_55[0][0]',
'conv2d_117[0][0]']
activation_174 (Activation) (None, 7, 7, 992) 0 ['concatenate_56[0][0]']
activation_175 (Activation) (None, 7, 7, 992) 0 ['activation_174[0][0]']
conv2d_118 (Conv2D) (None, 7, 7, 128) 126976 ['activation_175[0][0]']
batch_normalization_119 (Batch (None, 7, 7, 128) 512 ['conv2d_118[0][0]']
Normalization)
activation_176 (Activation) (None, 7, 7, 128) 0 ['batch_normalization_119[0][0]']
conv2d_119 (Conv2D) (None, 7, 7, 32) 36864 ['activation_176[0][0]']
concatenate_57 (Concatenate) (None, 7, 7, 1024) 0 ['concatenate_56[0][0]',
'conv2d_119[0][0]']
batch_normalization_120 (Batch (None, 7, 7, 1024) 4096 ['concatenate_57[0][0]']
Normalization)
activation_177 (Activation) (None, 7, 7, 1024) 0 ['batch_normalization_120[0][0]']
global_average_pooling2d (Glob (None, 1024) 0 ['activation_177[0][0]']
alAveragePooling2D)
dense (Dense) (None, 1000) 1025000 ['global_average_pooling2d[0][0]'
]
==================================================================================================
Total params: 7,936,424
Trainable params: 7,915,816
Non-trainable params: 20,608
__________________________________________________________________________________________________
3.10.编译模型
#设置初始学习率
initial_learning_rate = 1e-4
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)
model.compile(optimizer=opt,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
3.11.训练模型
'''训练模型'''
epochs = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
训练记录如下：
Epoch 1/10
15/15 [==============================] - ETA: 0s - loss: 5.4243 - accuracy: 0.4336
Epoch 1: val_accuracy improved from -inf to 0.33628, saving model to best_model.h5
15/15 [==============================] - 14s 251ms/step - loss: 5.4243 - accuracy: 0.4336 - val_loss: 5.9027 - val_accuracy: 0.3363
Epoch 2/10
14/15 [===========================>..] - ETA: 0s - loss: 2.7611 - accuracy: 0.7366
Epoch 2: val_accuracy improved from 0.33628 to 0.41372, saving model to best_model.h5
15/15 [==============================] - 3s 199ms/step - loss: 2.7560 - accuracy: 0.7367 - val_loss: 5.6493 - val_accuracy: 0.4137
Epoch 3/10
15/15 [==============================] - ETA: 0s - loss: 1.3179 - accuracy: 0.8319
Epoch 3: val_accuracy did not improve from 0.41372
15/15 [==============================] - 3s 181ms/step - loss: 1.3179 - accuracy: 0.8319 - val_loss: 5.2275 - val_accuracy: 0.4071
Epoch 4/10
15/15 [==============================] - ETA: 0s - loss: 0.7476 - accuracy: 0.8562
Epoch 4: val_accuracy improved from 0.41372 to 0.45354, saving model to best_model.h5
15/15 [==============================] - 3s 204ms/step - loss: 0.7476 - accuracy: 0.8562 - val_loss: 4.9389 - val_accuracy: 0.4535
Epoch 5/10
15/15 [==============================] - ETA: 0s - loss: 0.4224 - accuracy: 0.9181
Epoch 5: val_accuracy did not improve from 0.45354
15/15 [==============================] - 3s 184ms/step - loss: 0.4224 - accuracy: 0.9181 - val_loss: 4.4976 - val_accuracy: 0.4137
Epoch 6/10
15/15 [==============================] - ETA: 0s - loss: 0.2802 - accuracy: 0.9447
Epoch 6: val_accuracy improved from 0.45354 to 0.57743, saving model to best_model.h5
15/15 [==============================] - 3s 197ms/step - loss: 0.2802 - accuracy: 0.9447 - val_loss: 3.8567 - val_accuracy: 0.5774
Epoch 7/10
15/15 [==============================] - ETA: 0s - loss: 0.2048 - accuracy: 0.9558
Epoch 7: val_accuracy did not improve from 0.57743
15/15 [==============================] - 3s 183ms/step - loss: 0.2048 - accuracy: 0.9558 - val_loss: 3.4727 - val_accuracy: 0.4801
Epoch 8/10
15/15 [==============================] - ETA: 0s - loss: 0.1514 - accuracy: 0.9690
Epoch 8: val_accuracy did not improve from 0.57743
15/15 [==============================] - 3s 184ms/step - loss: 0.1514 - accuracy: 0.9690 - val_loss: 2.5986 - val_accuracy: 0.5619
Epoch 9/10
15/15 [==============================] - ETA: 0s - loss: 0.0868 - accuracy: 0.9912
Epoch 9: val_accuracy did not improve from 0.57743
15/15 [==============================] - 3s 181ms/step - loss: 0.0868 - accuracy: 0.9912 - val_loss: 2.1531 - val_accuracy: 0.5619
Epoch 10/10
15/15 [==============================] - ETA: 0s - loss: 0.0547 - accuracy: 0.9889
Epoch 10: val_accuracy did not improve from 0.57743
15/15 [==============================] - 3s 181ms/step - loss: 0.0547 - accuracy: 0.9889 - val_loss: 1.9243 - val_accuracy: 0.5243
3.12.模型评估
'''模型评估'''
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(len(loss))
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
3.13.图像预测
'''指定图片进行预测'''
# 采用加载的模型（new_model）来看预测结果
plt.figure(figsize=(10, 5)) # 图形的宽为10高为5
plt.suptitle("预测结果展示", fontsize=10)
for images, labels in val_ds.take(1):
for i in range(8):
ax = plt.subplot(2, 4, i + 1)
# 显示图片
plt.imshow(images[i].numpy().astype("uint8"))
# 需要给图片增加一个维度
img_array = tf.expand_dims(images[i], 0)
# 使用模型预测图片中的人物
predictions = model.predict(img_array)
plt.title(class_names[np.argmax(predictions)], fontsize=10)
plt.axis("off")
plt.show()
4 知识点详解
4.1 DenseNet算法详解
4.1.1 前言
在计算机视觉领域，卷积神经网络（CNN）已经成为最主流的方法，比如GoogleNet，VGG-16，Incepetion等模型。CNN史上的一个里程碑事件是ResNet模型的出现，ResNet可以训练出更深的CNN模型，从而实现更高的准确率。ResNet模型的核心是通过建立前面层与后面层之间的“短路连接”（shortcut, skip connection），进而训练出更深的CNN网络。
DenseNet模型的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的紧密连接（dense connection），它的名称也是由此而来。DenseNet的另一大特色是通过特征在channel上的的连接来实现特征重用（feature reuse）。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能，DenseNet也因此斩获CVPR2017的最佳论文奖。
其中DenseNet论文原文地址为：https://arxiv.org/pdf/1608.06993v5.pdf
4.1.2 设计理念
相比ResNet，DenseNet提出了一个更激进的密集连接机制：即互相连接所有的层，具体来说就是每个层都会接受前面所有层作为额外的输入。
图3为ResNet网络的残差连接机制，作为对比，图4为DenseNet的密集连接机制。可以看到，ResNet是每个层与前面的某层（一般是2~4层）短路连接在一起，连接方式是通过元素相加。而在DenseNet中，每个层都会与前面所有层在channel维度上链接（concat）在一起（即元素叠加），并作为下一层的输入。
对于一个 L L L层的网络，DenseNet共包含 L ( L + 1 ) 2 \frac{L(L+1)}{2} 2L(L+1)个连接，相比ResNet，这是一种密集连接。而且DenseNet是直接concat来自不同层的特征图，这可以实现特征重用，提升效率，这一特点是DenseNet与ResNet最主要的区别。
4.1.2.1 标准神经网络
图2是一个标准的神经网络传播过程示意图，输入和输出的公式是 X l = H l ( X l − 1 ) X_l=H_l(X_{l-1}) Xl=Hl(Xl−1)，其中 H l H_l Hl是一个组合函数，通常包括BN、ReLu、Pooling、Conv等操作， X l − 1 X_{l-1} Xl−1是第 l l l层的输入的特征图（来自于l-1层的输出）, X l X_l Xl是第 l l l层的输出的特征图。
4.1.2.2 ResNet
图3是ResNet的网络连接机制，由图可知是跨层相加，输入和输出的公式是 X l = H l ( X l − 1 ) + X l − 1 X_l=H_l(X_{l-1})+X_{l-1} Xl=Hl(Xl−1)+Xl−1
4.1.2.3 DenseNet
图4为DenseNet的连接机制，采用跨通道的concat的形式连接，会连接前面所有层作为输入，输入和输出的公式是
X
l
=
H
l
(
X
0
,
X
1
,
.
.
.
,
X
l
−
1
)
X_l=H_l(X_0,X_1,...,X_{l-1})
Xl=Hl(X0,X1,...,Xl−1)。这里要注意所有层的输入都来源于前面所有层在channel维度的concat，以下动图形象表示这一操作。
4.1.3 网络结构
网络的具体实现细节如图6所示。
CNN网络一般要经过Pooling或者stride>1的Conv来降低特征图的大小，而DenseNet的密集连接方式需要特征图大小保持一致。为了解决这个问题，DenseNet网络中使用DenseBlock+Transition的结构，其中DenseBlock是包含很多层的模块，每个层的特征图大小相同，层与层之间采用密集连接方式。而Transition层是连接两个相邻的DenseBlock，并且通过Pooling使特征图大小降低。图7给出了DenseNet的网络结构，它共包含4个DenseBlock，各个DenseBlock之间通过Transition层连接在一起。
在DenseBlock中，各个层的特征图大小一致，可以在channel维度上连接。DenseBlock中的非线性组合函数 H ( ⋅ ) H(\cdot) H(⋅)的是BN+ReLU+3x3Conv的结构，如图8所示。另外，与ResNet不同，所有DenseBlock中各个层卷积之后均输出k个特征图，即得到的特征图的channel数为k，或者说采用k个卷积核。k在DenseNet称为growth rate，这是一个超参数。一般情况下使用较小的k（比如12），就可以得到较佳的性能。假定输入层的特征图的channel数为 k 0 k_0 k0，那么 l l l层输入的channel数为 k 0 + k ( 1 , 2 , . . . , l − 1 ) k_0+k_{(1,2,...,l-1)} k0+k(1,2,...,l−1)，因此随着层数的增加，尽管设定的较小，DenseBlock的输入会非常多，不过这是由于特征重用所造成的，每个层仅有个k特征是自己独有的。
由于后面层的输入会非常大，DenseBlock内部采用bottleneck层来减少计算量，主要是原有的结构中增加1x1Conv，如图9所示，即BN+ReLU+1x1Conv+BN+ReLU+3x3Conv，称为DenseNet-B结构。其中1x1Conv得到4k个特征图，它起到的作用是降低特征数量，从而提升计算效率。
对于Trasition层，它主要是连接两个相邻的DenseBlock，并且降低特征图大小。Transition层包括一个1x1的卷积和2x2的AvgPooling，结构为BN+ReLU+1x1Conv+2x2AvgPooling。另外，Transition层可以起到压缩模型的作用。假定Transition层的上接DenseBlock得到特征图channels数为 m m m，Transition层可以产生个 ⌊ θ m ⌋ \lfloor\theta_m\rfloor ⌊θm⌋特征（通过卷积层），其中 θ ∈ ( 0 , 1 ] \theta\in(0,1] θ∈(0,1]是压缩系数（compression rate）。当 θ < = 1 \theta<=1 θ<=1时，特征个数经过Transition层没有变化，即无压缩，而当压缩系数小于1时，这种结构称为DenseNet-C，文中使用 θ = 0.5 \theta=0.5 θ=0.5。对于使用bootleneck层的DenseBlock结构和压缩系数小于1的Transition组合机构称为DenseNet-BC。
对于ImageNet数据集，图片输入大小为224x224，网络结构采用包含4个DenseBlock的DenseNet-BC，其首先是一个stride=2的7x7卷积层，然后是一个stride=2的3x3MaxPooling层，后面才进入DenseBlock。ImageNet数据集所采用的网络配置如表1所示：
4.1.4 效果对比
4.1.5 使用Pytroch实现DenseNet121
图11为DenseNet121的具体网络结构，它与表1中的DenseNet121相对应。左边是整个DenseNet121的网络结构，其中粉色为DenseBlock，最右侧为其详细结构，灰色为Transition，中间为其详细结构。
这里我们采用Pytorch框架来实现DenseNet，首先实现DenseBlock中的内部结构，这里是BN+ReLU+1x1Conv+BN+ReLU+3x3Conv结构，最后也加入dropout层以用于训练过程。
选择不同网络参数，就可以实现不同深度的DenseNet。
DenseNet121网络结构图
总结
通过本文的学习，分别采用pytorch和tensorflow框架实现desennet算法，发现采用pytorch可以实现很高的识别率，而tenserflow只能达到50%左右，经过查阅相关资料，发现有可能是tensorflow框架中BatchNormalization层有问题，测试数据采用训练数据，发现在屏蔽BatchNormalization层后，测试精度与训练精度差不多，而保留BatchNormalization层，测试精度就很低，可以确定就是BatchNormalization的原因。之前就有看到tensorflow 2.3版本BN层有问题，而tensorflow 1.x则正常。因此在需要使用tensorflow 搭建网络时，尽量选择1.x版本，本人未对该结果进行验证。
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